{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import cv2\n",
    "\n",
    "import torch.nn.functional as F\n",
    "from torch.utils import data\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "import os\n",
    "\n",
    "import glob\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"  \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_pics=glob.glob(r\"C:\\Users\\Administrator\\Desktop\\train\\*.TIF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "images=[p for p in all_pics if 'matte' not in p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "anno=[p for p in all_pics if 'matte' in p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "933"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(2021)\n",
    "index=np.random.permutation(len(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "images=np.array(images)[index]\n",
    "anno=np.array(anno)[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['C:\\\\Users\\\\Administrator\\\\Desktop\\\\train\\\\0516.TIF',\n",
       "        'C:\\\\Users\\\\Administrator\\\\Desktop\\\\train\\\\0157.TIF'], dtype='<U45'),\n",
       " array(['C:\\\\Users\\\\Administrator\\\\Desktop\\\\train\\\\0516_matte.TIF',\n",
       "        'C:\\\\Users\\\\Administrator\\\\Desktop\\\\train\\\\0157_matte.TIF'],\n",
       "       dtype='<U51'))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images[:2],anno[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_test_pics=glob.glob(r\"C:\\Users\\Administrator\\Desktop\\test\\*.TIF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images=[p for p in all_pics if 'matte' not in p]\n",
    "test_anno=[p for p in all_pics if 'matte' in p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform=transforms.Compose([\n",
    "    transforms.Resize((512,512)),\n",
    "    transforms.ToTensor()\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class hk_dataset(data.Dataset):\n",
    "    def __init__(self,imgs_path,annos_path):\n",
    "        self.imgs_path=imgs_path\n",
    "        self.annos_path=annos_path\n",
    "    def __getitem__(self,index):\n",
    "        img=self.imgs_path[index]\n",
    "        anno=self.annos_path[index]\n",
    "        pil_img=Image.open(img)\n",
    "        img_tensor=transform(pil_img)\n",
    "        anno_img=Image.open(anno)\n",
    "        anno_tensor=transform(anno_img)\n",
    "        anno_tensor[anno_tensor>0]=1\n",
    "        anno_tensor=torch.squeeze(anno_tensor).type(torch.long)\n",
    "        return img_tensor,anno_tensor\n",
    "    def __len__(self):\n",
    "        return len(self.imgs_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds=hk_dataset(images,anno)\n",
    "test_ds=hk_dataset(test_images,test_anno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCHSIZE=8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl=data.DataLoader(train_ds,batch_size=BATCHSIZE,shuffle=True)\n",
    "test_dl=data.DataLoader(test_ds,batch_size=BATCHSIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_batch,anno_batch=next(iter(train_dl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([8, 3, 512, 512]), torch.Size([8, 512, 512]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_batch.shape,anno_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "img=img_batch[0].permute(1,2,0).numpy()\n",
    "anno=anno_batch[0].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f99c054790>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(img)\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(anno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Downsample(nn.Module):\n",
    "    def __init__(self,in_channels,out_channels):\n",
    "        super(Downsample,self).__init__()\n",
    "        self.conv_relu=nn.Sequential(\n",
    "                                    nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1),\n",
    "                                    nn.ReLU(inplace=True),\n",
    "                                    nn.Conv2d(out_channels,out_channels,kernel_size=3,padding=1),\n",
    "                                    nn.ReLU(inplace=True),\n",
    "        )\n",
    "        self.pool=nn.MaxPool2d(kernel_size=2)\n",
    "    \n",
    "    def forward(self,x,is_pool=True):\n",
    "        if is_pool:\n",
    "            x=self.pool(x)\n",
    "        x= self.conv_relu(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Upsample(nn.Module):\n",
    "    def __init__(self,channels):\n",
    "        super(Upsample,self).__init__()\n",
    "        self.conv_relu=nn.Sequential(\n",
    "                                    nn.Conv2d(2*channels,channels,kernel_size=3,padding=1),\n",
    "                                    nn.ReLU(inplace=True),\n",
    "                                    nn.Conv2d(channels,channels,kernel_size=3,padding=1),\n",
    "                                    nn.ReLU(inplace=True),\n",
    "        )\n",
    "        self.upconv=nn.Sequential(\n",
    "                                nn.ConvTranspose2d(channels,channels//2,\n",
    "                                                  kernel_size=3,\n",
    "                                                  stride=2,\n",
    "                                                  padding=1,\n",
    "                                                  output_padding=1),\n",
    "                                nn.ReLU(inplace=True)\n",
    "        )\n",
    "    def forward(self,x):\n",
    "        x=self.conv_relu(x)\n",
    "        x=self.upconv(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DoubleConv(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch):\n",
    "        super(DoubleConv, self).__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv2d(in_ch, out_ch, 3, padding=1),\n",
    "            nn.BatchNorm2d(out_ch),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(out_ch, out_ch, 3, padding=1),\n",
    "            nn.BatchNorm2d(out_ch),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        return self.conv(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class UNetPP(nn.Module):\n",
    "    def __init__(self, in_channel=3, out_channel=2):\n",
    "        super().__init__()\n",
    "\n",
    "        nb_filter = [32, 64, 128, 256, 512]\n",
    "\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n",
    "\n",
    "        self.conv0_0 = DoubleConv(in_channel, nb_filter[0])\n",
    "        self.conv1_0 = DoubleConv(nb_filter[0], nb_filter[1])\n",
    "        self.conv2_0 = DoubleConv(nb_filter[1], nb_filter[2])\n",
    "        self.conv3_0 = DoubleConv(nb_filter[2], nb_filter[3])\n",
    "        self.conv4_0 = DoubleConv(nb_filter[3], nb_filter[4])\n",
    "\n",
    "        self.conv0_1 = DoubleConv(nb_filter[0] + nb_filter[1], nb_filter[0])\n",
    "        self.conv1_1 = DoubleConv(nb_filter[1] + nb_filter[2], nb_filter[1])\n",
    "        self.conv2_1 = DoubleConv(nb_filter[2] + nb_filter[3], nb_filter[2])\n",
    "        self.conv3_1 = DoubleConv(nb_filter[3] + nb_filter[4], nb_filter[3])\n",
    "\n",
    "        self.conv0_2 = DoubleConv(nb_filter[0] * 2 + nb_filter[1], nb_filter[0])\n",
    "        self.conv1_2 = DoubleConv(nb_filter[1] * 2 + nb_filter[2], nb_filter[1])\n",
    "        self.conv2_2 = DoubleConv(nb_filter[2] * 2 + nb_filter[3], nb_filter[2])\n",
    "\n",
    "        self.conv0_3 = DoubleConv(nb_filter[0] * 3 + nb_filter[1], nb_filter[0])\n",
    "        self.conv1_3 = DoubleConv(nb_filter[1] * 3 + nb_filter[2], nb_filter[1])\n",
    "\n",
    "        self.conv0_4 = DoubleConv(nb_filter[0] * 4 + nb_filter[1], nb_filter[0])\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.final = nn.Conv2d(nb_filter[0], out_channel, kernel_size=1)\n",
    "\n",
    "    def forward(self, input):\n",
    "        x0_0 = self.conv0_0(input)\n",
    "        x1_0 = self.conv1_0(self.pool(x0_0))\n",
    "        x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1))\n",
    "\n",
    "        x2_0 = self.conv2_0(self.pool(x1_0))\n",
    "        x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1))\n",
    "        x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1))\n",
    "\n",
    "        x3_0 = self.conv3_0(self.pool(x2_0))\n",
    "        x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1))\n",
    "        x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1))\n",
    "        x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1))\n",
    "\n",
    "        x4_0 = self.conv4_0(self.pool(x3_0))\n",
    "        x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))\n",
    "        x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1))\n",
    "        x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1))\n",
    "        x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1))\n",
    "\n",
    "        output = self.final(x0_4)\n",
    "        output = self.sigmoid(output)\n",
    "        return output\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=UNetPP().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNetPP(\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (up): Upsample(scale_factor=2.0, mode=bilinear)\n",
       "  (conv0_0): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv1_0): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv2_0): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv3_0): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv4_0): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv0_1): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv1_1): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv2_1): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv3_1): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv0_2): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv1_2): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv2_2): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv0_3): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv1_3): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(320, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (conv0_4): DoubleConv(\n",
       "    (conv): Sequential(\n",
       "      (0): Conv2d(192, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (sigmoid): Sigmoid()\n",
       "  (final): Conv2d(32, 2, kernel_size=(1, 1), stride=(1, 1))\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn=nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim import lr_scheduler\n",
    "optimizer=torch.optim.Adam(model.parameters(),lr=0.001)\n",
    "exp_lr_scheduler=lr_scheduler.StepLR(optimizer,step_size=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epoch, model, trainloader, testloader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    running_loss = 0\n",
    "    \n",
    "    model.train\n",
    "    for x, y in trainloader:\n",
    "        if torch.cuda.is_available():\n",
    "            x, y = x.to('cuda'), y.to('cuda')\n",
    "        y_pred = model(x)\n",
    "        loss = loss_fn(y_pred, y)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        with torch.no_grad():\n",
    "            y_pred = torch.argmax(y_pred, dim=1)\n",
    "            correct += (y_pred == y).sum().item()\n",
    "            total += y.size(0)\n",
    "            running_loss += loss.item()\n",
    "      \n",
    "    exp_lr_scheduler.step()\n",
    "    epoch_loss = running_loss / len(trainloader.dataset)\n",
    "    epoch_acc = correct / (total*512*512)\n",
    "        \n",
    "        \n",
    "    test_correct = 0\n",
    "    test_total = 0\n",
    "    test_running_loss = 0 \n",
    "    \n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for x, y in testloader:\n",
    "            if torch.cuda.is_available():\n",
    "                x, y = x.to('cuda'), y.to('cuda')\n",
    "            y_pred = model(x)\n",
    "            loss = loss_fn(y_pred, y)\n",
    "            y_pred = torch.argmax(y_pred, dim=1)\n",
    "            test_correct += (y_pred == y).sum().item()\n",
    "            test_total += y.size(0)\n",
    "            test_running_loss += loss.item()\n",
    "    \n",
    "    epoch_test_loss = test_running_loss / len(testloader.dataset)\n",
    "    epoch_test_acc = test_correct / (test_total*512*512)\n",
    "    \n",
    "        \n",
    "    print('epoch: ', epoch, \n",
    "          'loss： ', round(epoch_loss, 3),\n",
    "          'accuracy:', round(epoch_acc, 3),\n",
    "          'test_loss： ', round(epoch_test_loss, 3),\n",
    "          'test_accuracy:', round(epoch_test_acc, 3)\n",
    "             )\n",
    "        \n",
    "    return epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:  0 loss：  0.061 accuracy: 0.914 test_loss：  0.054 test_accuracy: 0.932\n",
      "epoch:  1 loss：  0.051 accuracy: 0.901 test_loss：  0.051 test_accuracy: 0.906\n",
      "epoch:  2 loss：  0.049 accuracy: 0.923 test_loss：  0.048 test_accuracy: 0.923\n",
      "epoch:  3 loss：  0.048 accuracy: 0.931 test_loss：  0.047 test_accuracy: 0.938\n",
      "epoch:  4 loss：  0.047 accuracy: 0.94 test_loss：  0.046 test_accuracy: 0.941\n",
      "epoch:  5 loss：  0.046 accuracy: 0.941 test_loss：  0.046 test_accuracy: 0.944\n"
     ]
    }
   ],
   "source": [
    "train_loss = []\n",
    "train_acc = []\n",
    "test_loss = []\n",
    "test_acc = []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    epoch_loss, epoch_acc, epoch_test_loss, epoch_test_acc = fit(epoch,\n",
    "                                                                 model,\n",
    "                                                                 train_dl,\n",
    "                                                                 test_dl)\n",
    "    train_loss.append(epoch_loss)\n",
    "    train_acc.append(epoch_acc)\n",
    "    test_loss.append(epoch_test_loss)\n",
    "    test_acc.append(epoch_test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 保存模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 在test数据集上进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH=r\"C:\\Users\\Administrator\\Desktop\\unetpp.pth\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(),PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model=UNetPP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image, mask = next(iter(test_dl))\n",
    "pred_mask = my_model(image)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(num):\n",
    "    plt.subplot(num, 3, i*num+1)\n",
    "    plt.imshow(image[i].permute(1,2,0).cpu().numpy())\n",
    "    plt.subplot(num, 3, i*num+2)\n",
    "    plt.imshow(mask[i].cpu().numpy())\n",
    "    plt.subplot(num, 3, i*num+3)\n",
    "    plt.imshow(torch.argmax(pred_mask[i].permute(1,2,0), axis=-1).detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r\"C:\\Users\\Administrator\\Desktop\\test1\\0425.TIF\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pil_img=Image.open(path)\n",
    "img_tensor=transform(pil_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_tensor_batch=torch.unsqueeze(img_tensor,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_tensor_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=my_model(img_tensor_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=torch.argmax(pred[0].permute(1,2,0),axis=-1).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(img_tensor.permute(1,2,0).numpy())\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_test=glob.glob(r\"C:\\Users\\Administrator\\Desktop\\prediction\\*.TIF\") \n",
    "print(all_test)\n",
    "for i in all_test:\n",
    "    path=i\n",
    "    pil_img=Image.open(path)\n",
    "    img_tensor=transform(pil_img)\n",
    "    img_tensor.shape\n",
    "    img_tensor_batch=torch.unsqueeze(img_tensor,0)\n",
    "    img_tensor_batch.shape\n",
    "    pred=my_model(img_tensor_batch)\n",
    "    pred.shape\n",
    "    pred[0].shape\n",
    "    pred=torch.argmax(pred[0].permute(1,2,0),axis=-1).numpy()\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.imshow(img_tensor.permute(1,2,0).numpy())\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.imshow(pred)\n",
    "    write_path=\"C://Users//Administrator//Desktop//test_r//\"+path[37:]\n",
    "    print(write_path)\n",
    "    cv2.imwrite(write_path,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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